Pyyan / Compare / LlamaIndex vs LangChain & LangGraph vs GraphRAG
LlamaIndex vs LangChain & LangGraph vs GraphRAG
3 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
| Specification | LlamaIndex | LangChain & LangGraph | GraphRAG |
|---|---|---|---|
| Summary | The strongest option for document-centric retrieval. | Orchestration, with retrieval as one piece. | Build a knowledge graph first, then query it. |
| Kind | Framework | Framework | Library |
| Graph | Yes, property graph index | Via integrations | Yes, core |
| Language | Python, TypeScript | Python, TypeScript | Python |
| Licence | MIT | MIT | MIT |
| GitHub stars | ~45k | ~120k | ~25k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | LlamaIndex ↗ | LangChain ↗ | Microsoft ↗ |
Highlighted rows are where these differ.
LlamaIndex
- Deep document handling and many indexing strategies
- The common advice is LlamaIndex for retrieval, LangGraph for orchestration
Best for PDFs, knowledge bases, structured data.
LangChain & LangGraph
- LangGraph adds durable state and checkpointing
- Widely used, and widely argued about
Best for agents that retrieve as one step among many.
GraphRAG
- Extracts entities and relationships, then summarises communities
- Answers “what are the themes here” which vector search cannot
- Expensive to index
Best for questions no single chunk can answer.